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Optica Quantum

Quantum optical reservoir computing powered by boson sampling

Akitada Sakurai, Aoi Hayashi, William J. Munro, Kae Nemoto

Published May 9, 2025• 12 citations
Publisher

Abstract

It is well known that boson sampling, a restricted non-universal quantum computation model, enables one to perform tasks that are hard to accomplish with digital computers. Boson sampling is associated with sampling the probability distribution of identical bosons passing through a random interferometer, and its quantum advantage has been demonstrated. It has, however, proven elusive to use such a model for practical applications. In this work, we show that the random interferometer powering boson sampling can be used to generate the complex dynamics necessary for quantum reservoir computing. We use these dynamics to perform various image recognition problems, illustrating the utility of the approach even for modest-size systems.

DOI
10.1364/opticaq.541432

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It is well known that boson sampling, a restricted non-universal quantum computation model, enables one to perform tasks that are hard to accomplish with digital computers. Boson sampling is associated with sampling the probability distribution of identical bosons passing through a random interferometer, and its quantum advantage has been demonstrated. It has, however, proven elusive to use such a model for practical applications. In this work, we show that the random interferometer powering boson sampling can be used to generate the complex dynamics necessary for quantum reservoir computing. We use these dynamics to perform various image recognition problems, illustrating the utility of the approach even for modest-size systems.
Quantum advantage has been demonstrated in a number of physical systems with boson sampling, a well-known restricted non-universal quantum computation model uniquely applicable to photonic systems, being one of the most significant examples. The boson sampling computational processors however has struggled to find real world practical applications they can be applied too. Here we show these processors can be used to perform various image recognition and classification tasks, even for modest sized systems. More specifically we show that these random interferometer-based boson sampling processors generate the complex dynamics necessary to power quantum reservoir computing.
We present a modular photonic approach to quantum reservoir computing, leveraging complex optical dynamics for efficient machine learning. Using architectures inspired by boson sampling, we show that random photonic interferometers can serve as powerful quantum reservoirs for tasks such as image classification. Modular connectivity—achieved through long-range and inter-module photonic links—enables scalable and high-performance learning while remaining compatible with integrated photonic platforms. Additionally, we propose a resource-efficient quantum neural network model that uses scale-free photonic connectivity to exploit the Hilbert space as a rich feature space. Our approach requires no optimization, supporting general-purpose use on fixed photonic hardware.
Abstract Boson Sampling is a task that is conjectured to be computationally hard for a classical computer, but which can be efficiently solved by linear-optical interferometers with Fock state inputs. Significant advances have been reported in the last few years, with demonstrations of small- and medium-scale devices, as well as implementations of variants such as Gaussian Boson Sampling. Besides the relevance of this class of computational models in the quest for unambiguous experimental demonstrations of quantum advantage, recent results have also proposed the first applications for hybrid quantum computing. Here, we introduce the adoption of non-linear photon–photon interactions in the Boson Sampling framework, and analyze the enhancement in complexity via an explicit linear-optical simulation scheme. By extending the computational expressivity of Boson Sampling, the introduction of non-linearities promises to disclose novel functionalities for this class of quantum devices. Hence, our results are expected to lead to new applications of near-term, restricted photonic quantum computers.
The paradigm of reservoir computing exploits the nonlinear dynamics of a physical reservoir to perform complex time-series processing tasks such as speech recognition and forecasting. Unlike other machine-learning approaches, reservoir computing relaxes the need for optimization of intra-network parameters, and is thus particularly attractive for near-term hardware-efficient quantum implementations. However, the complete description of practical quantum reservoir computers requires accounting for their placement in a quantum measurement chain, and its conditional evolution under measurement. Consequently, training and inference has to be performed using finite samples from obtained measurement records. Here we describe a framework for reservoir computing with nonlinear quantum reservoirs under continuous heterodyne measurement. Using an efficient truncated-cumulants representation of the complete measurement chain enables us to sample stochastic measurement trajectories from reservoirs of several coupled nonlinear bosonic modes under strong excitation. This description also offers a mathematical basis to directly compare the computational capabilities of a given physical reservoir operated across classical and quantum regimes. Applying this framework to the classification of quantum states of systems that are part of the same measurement chain as the quantum reservoir computer, we assess and explain measurement-contingent advantages and disadvantages of reservoir processing in quantum regimes. Our results also identify the vicinity of bifurcation points as presenting optimal nonlinear processing regimes of an oscillator-based quantum reservoir. The considered models are directly realizable in modern circuit QED experiments, while the framework is applicable to more general quantum nonlinear reservoirs.
Abstract It is proven that Boson sampling is a much promising model of optical quantum computation, which is applied to designing quantum computer successfully, such as “Jiuzhang” . However, the meaningful randomness of Boson sampling results has not been utilized or exploited. In this research, Boson sampling is applied to design a quantum random number generator (QRNG) by fully exploiting the randomness of Boson sampling results, and its prototype system is constructed with the programmable silicon photonic processor, which can generate unbiased random sequences and overcome the shortcomings of the existing discrete QRNGs such as source‐restricted, high demand for the photon number resolution capability of detector and slow self‐detection generator speed. Boson sampling is implemented as a random entropy source, and random bit strings with satisfactory randomness and uniformity can be obtained after post‐processing the sampling results. It is the first approach for applying the randomness of Boson sampling results to develop a practical prototype system for actual tasks, and the experiment results demonstrate that the designed Boson sampling‐based QRNG prototype system passes 15 tests of the NIST SP 800‐22 statistical test component, which proves that Boson sampling has great potential for practical applications with desirable performance besides quantum advantage.